Objective: The primary aim of this study is to examine and compare the classification performance of support vector machine models generated by various core functions used to classify diabetes mellitus in acute coronary syndrome patients. The secondary aim_ is to optimize the parameters of the various kernel functions which are used for constructing the support vector machine model and to achieve the best classification performance.

Conclusion: When the performance metrics were taken into account, the best classification performance was achieved from the Laplace Support Vector Machine model. In subsequent studies, the use of Support Vector Machine models with different kernel functions and other machine learning or data mining algorithms in different clinical trials may improve the classification success of the diseases.